Financial forecasting accuracy is one of the most critical aspects of successful business scaling. Ultimately, understanding where your business is going and how your business is doing informs management about essential decisions related to hiring, expansion and line of business.
Many fast-growing companies have difficulty making accurate and reliable financial forecasts. Making strategic decisions and managing the expectations of investors and other stakeholders is challenging.
We developed this comprehensive guide to help you improve your financial forecasting accuracy. It includes 12 tips to help you improve the accuracy of your financial forecasting.
First, let's discuss the challenges financial professionals face when forecasting the financial futures of their organisations. We'll also cover what modern approach will most improve forecasting accuracy.
Financial professionals aren't capable of predicting the future (it would be nice if we could!). However, when they use historical data and have the right technology infrastructure in place early on, financial teams can improve analytics predictability and the accuracy of their predictions for the future.
According to FP&A experts, a forecast is accurate if the actual numbers tend to match the forecast with "small" deviations.
Another definition they highlight is "Forecast is accurate when all assumptions and decisions are correct, accurately captured, sized and timed."
These two definitions demonstrate that small differences between forecast and reality are perfectly normal. However, the closer the finance team gets to their forecast estimate, the better their financial planning will be.
By applying the right processes, finance teams can perform an analysis of variance to investigate these discrepancies and ultimately minimise inaccuracies in their forecasting efforts.
Companies make financial projections for assorted reasons, such as managing their businesses, what-if scenarios, understanding working capital requirements and more.
With accurate financial forecasts, businesses can easily achieve their short-term and long-term goals.
Finance specialists help businesses keep up with their forecasts. But preparing a financial forecast for a business is no easy task. It is a complex analytical process with many challenges and limitations. Fortunately, there are online tools that can assist in creating accurate financial forecasts.
The shorter the time, the more accurate the financial forecasts. The longer the time, the less accurate and more challenging financial forecasting becomes. In general, difficulties are less likely in the short term and more difficult in the long run.
Forecasting for the next five years with data from prior years will make the process less challenging for the next 10 years. There may even be differences in the market shares or economic conditions of companies. Simply put, a shorter forecast period will always be more accurate than a longer forecast period.
Collecting and aggregating all the financial data of companies to go further can never be easy. This task can take a week to several weeks to gather all the information needed to build cash flow and revenue forecasts.
What are the latest upcoming proposals likely to be exchanged? What are the start dates? Is there a chance to slip? This type of request may seem more difficult for the sales team.
Collecting this data for forecasting purposes is one of the most significant problems in financial forecasting. And thinking about the customer is a bit difficult.
We cannot easily predict these things.
Forecasts using linear analysis may be commonplace, but this type of forecasting does not account for future uncertainty. In statistics, linearity is necessary when making certain assumptions about the future.
However, there is no guarantee that the relationship between the two variables will continue. Essentially, "noisy" data can create correlations that do not exist.
Many factors come into play when making forecasts, especially on important topics. Human error (which is common) can mean millions of dollars in variance in incorrect predictions.
Another problem with financial forecasting is unpredictability. Although businesses employ quantitative and qualitative forecasting techniques to make accurate predictions, unpredictability will continue to create problems.
These components are inherently subject to change and subject to forecast risk. For example, consider a retail store opening with a financial growth pillar. It, in turn, affects other retail stores in the particular area. It is something we will never be able to predict.
There is a solution to this challenge. We'll cover it more when we talk about using "Special Events" to account for anomalies.
Financial forecasting entails using past business data to predict the future. Assuming your average business growth of 10% has been steady over the past four years, you can forecast that your business capital in the next four years will be 10%. But using this kind of system could create a mountain of financial forecasting problems.
If a company has results that vary yearly, it makes no sense to use data from the previous period. In addition, financial data will not be available for startups as they will have to go through a rough estimate without any definite idea.
There are also cases where financial budgeting and projections are affected by external market events, which you do not capture by analyzing recorded results.
We prepared a list of tips below to improve the accuracy of your financial forecasting within 30 days. Many require implementing FP&A software or upgrading your legacy systems. You can also use these tips to periodically fine-tune your forecasting processes.
1. Understanding Your Organisation and Its Key Drivers
One of the simple approaches to understanding your business better is to look at income and expense flows. Which aspects are variable and which stay the same?
Answering these questions will help you identify the key drivers of change. The process doesn't need to be complicated, and, in most cases, the answer depends on the proportions and volumes.
For example, with sales, you need to realise the relationship between future price and volume. Along with the cost, you need to understand the unit cost and the vast step functions that happen with scale.
Conventional financial drivers you need to know about include the company's recent history and common sense in your industry. The metrics for these drivers include:
Do not limit yourself to these, as there may be other factors that significantly impact your particular business.
It is impossible to make a consistent forecast without something to measure. Try to get as close to the actual source of operational data as possible that is already well-defined in your system.
It will allow you to ensure that it has not been tampered with or altered. The fewer barriers there are between the data and you, the more accurate and valuable that data will be.
Ensure you comprehend what the data stands for exactly (i.e., what the data is collecting) and are mindful of the larger context. While circumstantial information can be useful, you must understand how data relates to global performance.
Part of this framework is when the data gets collected and the period it covers. It can vary depending on how IT teams could integrate the database at your organisation.
Data must be accessible periodically and interpreted with consistent and reasonable definitions. For example, you need to know if the data record of the area you are applying gets permanently timestamped or if it is the latest record version.
The Garbage In, Garbage Out principle applies to forecasting and many things. In this case, "Garbage In" is the historical data used by the forecasting engine to generate the forecast.
Poor historical data is by far the most common cause of forecast errors. Only by ensuring you have a clean amount of data in the forecaster can you hope to get good results.
Cleanup is fixing errors with your historical data. Errors can get introduced into your data in several ways. You need to find a way to remove dirty data (bad values) from your data and replace them with correct values. That usually involves finding a second dataset to compare with the data you're currently using.
When cleaning dirty data, you should not invent data, estimate the outcome or remove bad values. If you know the exact value, you should correct the error. Let the error be wrong if you do not have the right answer.
Be sure to use special dates or, if possible, let the forecasting method handle it. The best way to avoid dirty data is to use an audited data source.
Except for sales, verified data is usually unavailable. Some of the driver data you need will not get checked. It may not be available at the desired granularity. Generating these verified figures may take time and the data will not be available when you need it for forecasting purposes
A final note on audited data. Just because you are using it does not mean it will not be error free. It usually means that there will be fewer errors and that the authority has accepted them.
Data cleaning will fix errors in the data, but what about data anomalies? What happens when sales spike due to significant discounts or when stores close because a blizzard engulfs the area?
These anomalies are not errors and cannot be "cleaned up." Instead, you should use a feature in virtually every Excel-based system called "Special Days" or "Special Events."
This feature allows you to mark certain days on the calendar as "special." Mark a date (or days) as a special reminder for either response from the analyst. The analyst ignores the special date or uses other relevant, unique dates to accurately forecast a single event.
Special Dates is a very powerful tool that we suggest you do not abuse because abuse of this powerful feature often hurts accuracy more than it helps.
The timing of the forecast—when the forecast gets generated relative to the forecast date—has a significant impact on forecast accuracy.
Specifically, creating a forecast one week from now will produce a more accurate forecast than a forecast created four weeks from now.
Generate forecasts as close as possible to the dates they represent. Ideally, you should create your forecast a day before you need it. Although this is not always possible, you can use this rolling forecast template to assist you.
With Vena, your forecast schedule is determined by when you need to post it to your associates in relation to when your schedule starts and the amount of time you want to allow for forecast review and schedule changes.
If you are creating forecasts more than two weeks in advance and experiencing problems with forecast accuracy, perform some analysis to determine if a newer forecast improves the situation.
The "Law of Large Numbers" tells us that more significant numbers produce more accurate results than smaller numbers. Applying this rule to forecasting means that more significant numbers will generate more accurate forecasts than smaller ones.
If you are still having problems with the accuracy of the forecasts in previous steps, you need to generate larger numbers for your forecasting engine to work. You make bigger numbers by varying the level of detail than you expect.
Instead of forecasting by product line, consider forecasting by product category or division. Instead of planning for a quarter hour, consider planning for a half hour or an hour.
Changing forecast granularity can be a tricky business, especially if done in a production environment. That usually involves modifying the data source and the import process.
It may involve consolidating labour standards. It may also require changing the driver and store hierarchy.
The changes associated with this step must be well thought out and planned. However, improving the accuracy of your predictions can be worth the effort.
So far, we have suggested improving data quality, resolving data anomalies, improving data timeliness and increasing data size. In short, we focused only on data.
Another critical step to improving forecast accuracy is to look beyond the data and to test the forecasting method. For many people, this feels counterintuitive.
The natural reaction is to look at the forecasting method in advance, wondering how the system might solve the problem.
The "Garbage In, Garbage Out" principle makes that focus null and void. Until you have clear data on which to base your forecasts, you will never have an accurate forecast, no matter what forecasting method you use.
Also, note that we did not call this step "Changing Your Forecasting Method." Instead, we suggest that you consider changing your forecasting method.
You have most likely performed an analysis to determine that a given prediction method is the right one for a particular driver, and at this point, you need to confirm that your initial analysis of the different prediction methods available to use is accurate.
If it is true, a different forecasting method would be better only because something has changed dramatically with your data.
This change could be due to:
To determine if a change in your forecasting method is warranted, you will need to perform your analysis again using the current clean data set.
Tracking ratio and volume back to revenue and cost factors is best. For example, forecasting methods work by reverse-engineering historical volumes and rates.
Determine which factors have the most influence and which ones you expect to influence forecast changes. If you accurately model past revenues and expenses with these historic factors, you can be relatively sure that your forecasting process is correct.
If you plan for multiple periods, ensure integrity by preventing reassessing after each term. Use this reevaluation to determine whether the forecast should need adjusting up or down.
Use historical contexts you develop to question current assumptions about future drivers. For example, assess how these assumptions relate to past performance if these assumptions come from sales.
After that, if significant adjustments to the way it works are warranted, verify the timing of those changes thoroughly to ensure they're realistic. The operations team must be able to provide a roadmap for anticipated changes in business dynamics.
As the financial owner of the process, you must ask questions that provide transparency about how the change will unfold. Remember that operations teams are solely responsible for the driver predictions they provide. The future driver should always have an owner, or your finance department will own it by default.
Verifying your data and assumptions with senior management ensures that those responsible for delivering results are fully aware of the situation and further mitigate risk.
An organization is ever-evolving. Financial projections must reflect these changes. Decisions based on outdated information can lead to bad decisions. If you rely solely on an annual forecast, you are relying on outdated information. Adding flexibility to your processes by switching to a rotational forecasting model will allow management to align with up-to-date data to support their strategic decision making.
Financial teams tend to use a laser-focused method when analyzing their business data. However, when creating your financial forecast, consider both the micro and macro environments in which your business operates.
Do not rely solely on historical data. Considering the competition and market conditions will enable your finance team to take a more holistic approach to their financial forecasting and planning initiatives.
Most finance teams take a top-down approach when conducting business forecasting. However, they seek input from all functions.
For example, talking to the Head of Sales may provide greater insight into the predicted revenue projections or actual demand from prospects. This gives the finance team a more holistic view of how the organisation operates and performs.
Many organisations will prioritise a corporate performance management strategy to better understand how each team works and supports one another.
When discussing competitors, you should analyse their performance to uncover trends, changes in strategy, opportunities or progress they have experienced over the past year.
By performing a competitor analysis using a benchmark report, you can conduct a side-by-side comparison to see how your business performs compared to other competitors in your industry and adjust your strategy accordingly.
Always be careful with your targets, ratios, volumes, rates and profits. Be practical, and when in doubt, use caution.
Above all, be careful to tie yourself to overly optimistic forecasts that derail your goals and jeopardise your integrity. A variety of forecast scenarios, such as probable-case, best-case, and worst-case scenarios, will get your organisation to think through the full range of prospects and provide you with better protection against errors.
If you still haven't gotten where you need to be, do not just change your assumptions. Make sure that what you are changing is something you can operate and ensure you are trying everything using real business data.
Ensure your accountants, financial planners and analysts work with the same terminology and definitions for financial measures so you can easily compare actual results with forecasts.
Having a common language for financial terms also avoids confusion throughout the organization. Finally, carefully measure the success of your forecasts, especially in terms of accuracy, and make improvements as needed.
With the right technical infrastructure and processes, any finance team can streamline forecasting efforts and set up their business for success.
For startups looking to raise capital, improving the accuracy of financial forecasts will also provide robust investor reports and build trust among investors in the future.
By upgrading your technology stack and using a centralized database for all your data, your financial analysts can generate reliable and accurate forecasts from a single source of truth.
After you try our recommendations, your financial forecasting will be as accurate as possible, given the limitations of your data, business processes and available forecasts.
The foundation of a successful business is a clear understanding of what works, how it works and what to do if a particular approach fails. This knowledge will allow you to build on past successes and make strategic decisions knowing that you've done everything possible to ensure a positive outcome.
Get more accurate financial forecasting with Vena FP&A Software and our industry-leading solutions. You'll also want to check out our free financial Excel templates available for everyone to use.
Get resources curated just for you and your department.Learn More